code: def mutESLogNormal(individual, c, indpb):
      r"""Mutate an evolution strategy according to its :attr:`strategy`
      attribute as described in [Beyer2002]_. First the strategy is mutated
      according to an extended log normal rule, :math:`\\boldsymbol{\sigma}_t =
      \\exp(\\tau_0 \mathcal{N}_0(0, 1)) \\left[ \\sigma_{t-1, 1}\\exp(\\tau
      \mathcal{N}_1(0, 1)), \ldots, \\sigma_{t-1, n} \\exp(\\tau
      \mathcal{N}_n(0, 1))\\right]`, with :math:`\\tau_0 =
      \\frac{c}{\\sqrt{2n}}` and :math:`\\tau = \\frac{c}{\\sqrt{2\\sqrt{n}}}`,
      the the individual is mutated by a normal distribution of mean 0 and
      standard deviation of :math:`\\boldsymbol{\sigma}_{t}` (its current
      strategy) then . A recommended choice is ``c=1`` when using a :math:`(10,
      100)` evolution strategy [Beyer2002]_ [Schwefel1995]_.
  
      :param individual: :term:`Sequence <sequence>` individual to be mutated.
      :param c: The learning parameter.
      :param indpb: Independent probability for each attribute to be mutated.
      :returns: A tuple of one individual.
  
      .. [Beyer2002] Beyer and Schwefel, 2002, Evolution strategies - A
         Comprehensive Introduction
  
      .. [Schwefel1995] Schwefel, 1995, Evolution and Optimum Seeking.
         Wiley, New York, NY
      """
      size = len(individual)
      t = c / math.sqrt(2.0 * math.sqrt(size))
      t0 = c / math.sqrt(2.0 * size)
      n = random.gauss(0, 1)
      t0_n = t0 * n
  
      for indx in range(size):
          if random.random() < indpb:
              individual.strategy[indx] *= math.exp(
                  t0_n + t * random.gauss(0, 1)
              )
              individual[indx] += individual.strategy[
                  indx
              ] * random.gauss(0, 1)
  
      return (individual,)
  
file_path: deap/deap/tools/mutation.py
imports: import math
  import random
  from itertools import repeat
label_desc: Use image node_id 7 for calling a global function with example usage: mutESLogNormal(individual, c, indpb) and returns: individual